Codificação de Rótulos is a technique used in pré-processamento de dados, specifically for converting categorical data into a numerical format that aprendizado de máquina algorithms can understand. It is particularly useful when dealing with categorical features that do not have an inherent order but need to be represented as numbers for treinamento de modelos.
In label encoding, each unique category value is assigned an integer value starting from 0. For example, if you have a variável categórica ‘Color’ with values [‘Red’, ‘Green’, ‘Blue’], label encoding would convert these to numerical values like:
- Vermelho -> 0
- Verde -> 1
- Azul -> 2
Essa transformação simples permite que algoritmos que dependem de entrada numérica processem os dados categóricos de forma eficaz.
However, it’s important to note that label encoding can introduce unintended ordinal relationships between categories. For instance, the model might mistakenly interpret ‘Red’ (0) as being less than ‘Green’ (1) and ‘Blue’ (2), which may not accurately reflect the nature of the data. To mitigate this issue, other encoding techniques like Codificação One-Hot might be used, particularly when the categorical variable is nominal (without a meaningful order).
No geral, a codificação de rótulos é um método simples para lidar com dados categóricos, tornando-se uma escolha comum em vários pipelines de aprendizado de máquina onde recursos categóricos estão presentes.